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Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach

Great data-driven insights start with great data quality.

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Contributors

Akin Akinwumi, Startech.com
Patrick Bossey, Crawford and Co.
Diraj Goel, Reach Social Inc.
Anonymous contributors

Your Challenge

With the ever-increasing demand from the business to make data-driven decisions, IT is struggling to ensure quality in the large volumes and variety of data.
IT consistently hears that the reports that the business relies on are not accurate, and they are required to perform time-intensive and costly manual fixes to the organization’s data.
Data users do not trust the data quality in your company. Business users bypass IT and establish their own data silos to manage quality. Without governance or consistency, data quality issues are propagated further within data silos.
Data is not usable or useful to the business due to deficiencies in quality and processes, resulting in decisions being made based on intuition and weak analysis. Worse, these decisions can be based on the wrong data.
With sub-optimal data quality, companies are unable to launch related data initiatives such as business intelligence, master data management, and big data. They need a clear understanding of business requirements and the implementation of data quality processes first.

Our Advice

Critical Insight

Data quality means tolerance, not perfection. One-hundred percent pure quality data does not exist. Instead, think of data quality as a level of error that is tolerated by the business. Tolerance for data quality is unique to each business unit, and “good enough” for the business must be determined before embarking on costly and lengthy data quality repairs. If the data allows the business to operate at the desired level, don’t waste time fixing data that may not need to be fixed.
Prevention is 10x cheaper than remediation. While going through this blueprint, you will learn how to address data quality issues at the root. Stop fixing data quality with band-aid solutions and start fixing it by healing it at the source of the problem. This will prevent costly and lengthy data quality repairs that require manual repairs. Address and resolve the problem at the point of ingestion and at process level to ensure going forward the data quality is up to standard.
Data quality without data governance is equivalent to treating the symptoms but not curing the disease. To ensure that data quality fixes address the root causes of the problems, as well as to maintain that mindset and effort throughout the organization, data quality processes must have the appropriate oversight and governance. If data quality is not embedded into the enterprise data governance framework, data quality management will remain a band-aid fix.

Impact and Result

Implement a set of data quality initiatives that are aligned with overall business objectives and aimed at addressing data practices and the data itself.
Develop a prioritized data quality improvement project roadmap and long-term improvement strategy.
Build related practices such as business intelligence and analytics with more confidence and less risk after achieving an appropriate level of data quality.

Schedule Analyst Call

Start here – read the Executive Brief

Read our concise Executive Brief to find out why your organization should be relying on its data to make decisions, and why that data needs to be of high quality to support downstream data-driven decision making.

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach – Executive Brief

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach – Phases 1-4

1. Define your organization’s data quality practice

Learn about what causes data quality issues, how to measure data quality health, and what makes a good data quality practice – and create a plan to improve it.

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach – Phase 1: Define Your Organization's Data Quality Practice

Data Quality Health Check Tool

Data Quality Practice Assessment and Project Planning Tool

2. Analyze your priorities for data quality fixes

Determine your business unit priorities to create data quality improvement projects.

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach – Phase 2: Analyze Your Priorities for Data Quality Fixes

3. Fix your data quality issues

Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit, then determine a strategy for fixing those issues.

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach – Phase 3: Fix Your Data Quality Issues

Data Lineage Diagram Template

Data Quality Checklist

Data Quality Improvement Plan Template

4. Sustain your data quality practices

Identify strategies for continuously monitoring and improving data quality at the organization.

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach – Phase 4: Sustain Your Data Quality Practices

Guided Implementations

This guided implementation is a nine call advisory process.

Guided Implementation #1 - Define your organization’s data quality practice

Call #1 - Learn about the concepts of data quality and the common root causes of poor data quality.

Call #2 - Identify the core capabilities of IT for improving data quality on an enterprise scale.

Call #3 - Create a strategy for improving these capabilities.

Guided Implementation #2 - Analyze your priorities for data quality fixes

Call #1 - Determine which business units use data and require data quality remediation.

Call #2 - Create a plan for addressing business unit data quality issues according to priority of the business units based on value and impact of data.

Guided Implementation #3 - Fix your data quality issues

Call #1 - Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit.

Call #2 - Determine a strategy for fixing data quality issues for the highest priority business unit.

Guided Implementation #4 - Sustain your data quality practices

Call #1 - Identify strategies for continuously monitoring and improving data quality at the organization.

Call #2 - Learn how to incorporate data quality practices in the organization’s larger data management and data governance frameworks.

Schedule Your Call

What is a guided implementation?

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Data Quality

A manifesto for strategic data quality improvement.
This course makes up part of the Data & BI Certificate.

Course information:

Title: Data Quality
Number of Course Modules: 5
Estimated Time to Complete: 2-2.5 hours
Featured Analysts:
Crystal Singh, Research Director, Applications
David Piazza, VP of Research & Advisory, Applications Practice
Now Playing: Academy: Data Quality | Executive Brief

Book Your Workshop

Onsite workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost onsite delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.

Module 1: Define and Assess Your Organization’s Data Quality Practice

The Purpose

Evaluate the maturity of the existing data quality practice and activities.
Assess how data quality is embedded into related data management practices.
Envision a target state for the data quality practice.

Key Benefits Achieved

Understanding of the current data quality landscape.
Gaps, inefficiencies, and opportunities in the data quality practice are identified.
Target state for the data quality practice is defined.

Activities

Outputs

1.1

Explain approach and value proposition.

Data Quality Management Primer

1.2

Detail business vision, objectives, and drivers.

Data Quality Organizational Business Context

1.3

Discuss data quality barriers, needs, and principles.

Data Quality Heath Check

1.4

Assess current enterprise-wide data quality capabilities.

1.5

Identify data quality practice future state.

1.6

Analyze gaps in data quality practice.

Module 2: Create a Strategy for Data Quality Project 1

The Purpose

Define improvement initiatives.
Define a data quality improvement strategy and roadmap.

Key Benefits Achieved

Improvement initiatives are defined.
Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
A roadmap is defined to depict when and how to tackle the improvement initiatives.

Activities

Outputs

2.1

Create business unit prioritization roadmap.

Business Unit Prioritization Roadmap

2.2

Develop subject area project scope.

Subject area scope

2.3

Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis.

Data Lineage Diagram

Module 3: Create a Strategy for Data Quality Project 2

The Purpose

Define improvement initiatives.
Define a data quality improvement strategy and roadmap.

Key Benefits Achieved

Improvement initiatives are defined.
Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
A roadmap is defined to depict when and how to tackle the improvement initiatives.

Activities

Outputs

3.1

Create business unit prioritization roadmap.

Business Unit Prioritization Roadmap

3.2

Develop subject area project scope.

Subject area scope

3.3

Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis.

Data Lineage Diagram

Module 4: Create a Plan for Sustaining Data Quality

The Purpose

Plan for continuous improvement in data quality.
Incorporate data quality management into the organization’s existing data management and governance programs.

Key Benefits Achieved

Sustained and communicated data quality program.

Activities

Outputs

4.1

Formulate metrics for continuous tracking of data quality and monitoring the success of the data quality improvement initiative.

Data Quality Practice Improvement Roadmap

4.2

Workshop debrief with project sponsor.

Data Quality Improvement Plan (for defined subject areas)

4.3

Meet with project sponsor/manager to discuss results and action items.

4.4

Wrap up outstanding items from the workshop, deliverables expectations, guided implementations.

Discuss This Workshop

After each Info-Tech experience, we ask our members to quantify the real time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this Blueprint, and what our clients have to say.

Client

Experience

Impact

$ Saved

Days Saved

Testimonial

Central Arizona Project

Guided Implementation

9/10

N/A

Libro Credit Union

Guided Implementation

9/10

N/A

This was just an introduction so unfortunately i can not quantify cost or dollar savings yet.

TriServe Tech

Guided Implementation

10/10

$13,236

Best => Give me an idea how to start with a Data Quality project Worst => N/A

The Regional Municipality of Peel

Guided Implementation

10/10

$10,000

Crystal is very knowledgeable in the Data quality practice. I was quite impressed with her swiftness in addressing my inquiries. While still formulating my question she was already pulling the appropriate material that matches exactly the information I needed. The experience was very delightful!

Festival Foods, Inc.

Guided Implementation

9/10

$132K

120

East Carolina University

Guided Implementation

10/10

N/A

Buckland Customs Brokers Ltd.

Guided Implementation

9/10

N/A

Motorsport Aftermarket Group

Guided Implementation

10/10

N/A

East Carolina University

Guided Implementation

10/10

N/A

East Carolina University

Guided Implementation

10/10

N/A

Excellent timing, materials, and delivery -- nothing negative to speak of here, for sure!

GAI Consultants, Inc.

Guided Implementation

10/10

$132K

115

It's hard to put a value on 'state of mind'. Steve Wilson conveys so much wisdom with his conversations, that every nugget of information is valuable. Best part of today's conversation was learning to embed data governance into the DNA of your data analytical system. Thank you.

Wood

Guided Implementation

8/10

N/A

Excellent delivery and valuable knowledge shared with us. This has helped shape the way we will proceed with the Data Quality workstream in my organisation

Miami Children's Hospital

Guided Implementation

8/10

$6,618

N/A

Excellent session, great recommendations for dashboard development

Burman University

Guided Implementation

3/10

N/A

Information was great, but probably not good timing for this project for us.

Parks Canada

Guided Implementation

6/10

$5,000

Best - validation that despite the organizational challenges, I'm on the right path. Worst - the validation that there is no quick fix for the organizational challenges....

Donor Network West

Workshop

8/10

$66,183

Pros: good process where numerous staff had a chance to speak and give their insight. Cons: It was hard to hear how unconnected staff are to our data needs

Surgi-Care, Inc.

Guided Implementation

6/10

N/A

It helped to talk to someone outside of the company about the challenges we are facing.

GIE AGRICA GESTION

Guided Implementation

6/10

$7,216

N/A

Young Presidents' Organization

Guided Implementation

5/10

N/A

Stewart was very helpful in discussing some of the data quality options that are out in the marketplace. Info-tech doesn't have a vendor landscape for data quality, which is a major pain point across a majority of companies. Would be great to see that in the future.